In 1967, Jocelyn Bell Burnell, a female scientist, “discovered pulsars” and published a paper alongside her adviser, Antony Hewish, a male scientist. Yet, only Hewish and Martin Ryle—other male colleagues—were “given the Nobel Prize for Physics in 1974 for the discovery of pulsars.” This is one of many past examples of Nobel Prize controversies, and with the recent revealing of the 2024 awardees, history has repeated itself. The Nobel Prize, considered the world’s most prestigious award, often attracts significant criticism. Whether it is political bias, sexism, lack of diversity, or failure to recognize major contributors to the research, there are numerous complaints.

Awardees, Nobel Laureates, are honored annually for their contributions to advocacy, advancement, and peace in six fields: Physics, Chemistry, Physiology or Medicine, Literature, Economics, and Peace. In the first week of October, the Nobel Laureates of 2024 were revealed, and as in the past, there are already controversies around these newly inducted Nobel Laureates, especially surrounding the field of Physics.

The two winners of the Nobel Prize in Physics 2024 are John J. Hopfield and Geoffery E. Hinton for their “foundational discoveries and inventions that enable machine learning with artificial neural networks.” John J. Hopfield, an American physicist and a former Physics professor at Princeton, invented a neural network—machine learning that mimics the human brain—that used a method for saving and recreating patterns—called the Hopfield Network—in 1982. Geoffery E. Hinton, known as the godfather of Artificial Intelligence (AI) for his early research into neural networks, was a former Google researcher who has made a drastic impact in the field of deep learning. One of his most significant milestones and the reason for his induction as a 2024 Nobel Laureate was the invention of the Boltzmann machine in 1985, a type of neural network that recognizes characteristics and features of a data set. 

The Controversies

Firstly, one large reason for the controversy is the fact that the Nobel Prize in Physics was awarded for machine learning, which surprised many since AI and machine learning aren’t “what comes to mind when most people think of physics.” AI is the field that involves creating machines and systems that perform tasks that require human intelligence, while machine learning is a subset of AI that focuses on the concept of machines being able to learn from data and improve over time as they acquire more and more data. Some argue that these inventions were more in the domain of the Turing Award— an ”annual award given by the Association for Computing Machinery (ACM)” for “contributions of a technical nature made to the computing community”—rather than the Nobel Prize.

Another reason for the controversy is their failure to recognize previous major contributors in machine learning that drastically assisted these Laureates with their “inventions.” For instance, Jürgen Schmidhuber—a German computer scientist and a catalyst for modern AI—has voiced the greatest opposition to the Nobel Prize, stating that the Nobel Prize in Physics 2024 “for Hopfield & Hinton rewards plagiarism and incorrect attribution in computer science.” He argues that there were previous machine learning pioneers—such as Alexey Ivakhnenko, Valentin Lapa, and Shun-Ichi Amari—who developed these machines before the prize recipients, pointing to his writing “Annotated History of Modern AI and Deep Learning” for evidence.

The Boltzmann Machines paper by Geoffrey E. Hinton explores the hidden units of neural networks. However, as mentioned in the “Annotated History of Modern AI and Deep Learning,” Ivakhnenko and Lapa “introduced the first general, working algorithms for deep LPs with “many hidden layers” in 1965. Furthermore, the Boltzmann machine is praised for its ability to recognize complex characteristics of data, which Schmidhuber denotes was first done by Ivakhnenko’s nets, which “learned to create hierarchical, distributed, internal representations of incoming data.” So, although Ivakhnenko and Lapa developed an invention similar to the Boltzmann machine 20 years prior, their hard labor was never credited or recognized in the development of the Boltzmann machine or the Nobel Prize awarded to Hinton for the machine.

Likewise, John J. Hopfield’s research mirrors that of Shun-Ichi Amari in 1972. Amari published his paper titled “Learning Patterns and Patterns Sequences by Self-Organizing Nets of Threshold Elements,” which focused on mathematical calculations that enabled neuron-like elements of a machine to learn static and dynamic patterns based on its connection weights. Ten years later, John J. Hopfield published a paper on a similar topic of a neural network’s method of recognizing and storing patterns, resulting in Amari’s discoveries being named under Hopfield’s name. Schmidhuber argues that Hopfield republished Amari’s paper without citing him.

Conclusion

The Nobel Prize, awarded to the most influential individuals in the world, is often filled with corruption, inequity, and exploitation. Whether it is bias, discrimination, or political involvement, there are many flaws in this so-called “prestigious honor.” Consequently, it results in various backlash and controversy. This year, it involved the Nobel Prize in Physics and the two winners’ plagiarism of previous machine learning pioneers who published similar works before their works. Machine learning and AI are considered a recent and emerging topic, thanks to Chat-GPT. However, this topic has been studied and researched for a very long time, as early as the 1920s. Thus, there are bound to be various works that are hidden because they were not considered important during the time of their publication, which may result in injustices such as plagiarism. The field of AI and machine learning is very important at this moment, and there is no question that it will continue to dominate; however, this also means the rise of various controversies surrounding this topic. The recent one involving the Nobel Prize shows no limitations to what topic this can reach.